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Looking at the top left of the re-ordered correlation matrix, we can see that the people who watch any one of the sports programs are more likely to watch one of the other sports programs. The table below shows the data again, but with the columns and rows re-ordered to reveal some patterns.
#XLSTAT PRINCIPAL COMPONENT ANALYSIS PROFESSIONAL#
5 between World of Sport and Professional Boxing is higher than the correlation of. For example, the table shows that people who watch World of Sport frequently are more likely to watch Professional Boxing frequently than are people who watch Today. The higher the correlation, the greater the overlap in the viewing of the programs. This shows the relationship between the viewing of the TV program shown in the row with that shown in the column. Each of the numbers in the table is a correlation. The table below shows a correlation matrix of the correlations between viewing of TV programs in the U.K. At a technical level, factor analysis and principal component analysis are different techniques, but the difference is in the detail rather than the broad interpretation of the techniques.
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However, they have broad application across data analysis, from finance through to astronomy. The most well-known application of these techniques is in identifying dimensions of personality in psychology. These latent variables are often referred to as factors, components, and dimensions. These patterns are used to infer the existence of underlying latent variables in the data. Factor analysis and principal component analysis identify patterns in the correlations between variables.